Spun multicore fiber has been used to determine the shape of an optical fiber. A multicore fiber having four cores can be used to separate the deformation of the fiber into two bend angles (pitch and yaw), one twist angle, and the fiber elongation. These four measurements constitute four degrees of freedom. These four measurements (pitch, yaw, twist, and elongation) also represent all of the deformations that can happen to the fiber with relatively small forces.
In fiber optic based shape sensing, a multi-channel distributed strain sensing system is used to detect the change in strain for each of several cores within a multicore optical shape sensing fiber as described in U.S. Pat. No. 8,773,650, incorporated herein by reference. Multiple distributed strain measurements are combined through a system of equations to produce a set of physical measurements including curvature, twist, and axial strain as described in U.S. Pat. No. 8,531,655, incorporated herein by reference. These physical measurements can be used to determine the distributed shape and position of the optical fiber.
Some applications for shape sensing fiber require a high degree of confidence or safety in terms of the accuracy and reliability of the shape sensing output. An example application is robotic arms used in fine manufacturing, surgical, or other environments.
Another problem with shape sensing fiber applications is unforeseen or unpredictable errors that are not included in shape sensing models or model assumptions. Example errors include errors in the operation of the optical and/or electronic sensing and processing circuitry, errors in connecting fibers, human errors such as loading an incorrect calibration file to calibrate the shape sensing system, and errors caused by forces experienced by the fiber that are not included in the shape sensing model. One such parameter already described is fiber pinch. Another parameter is temperature if the shape sensing model does not account for changes due to temperature. A further concern is other parameters not yet known or identifiable. So a further need is for the technological solution to be able to detect errors that are independent from and not accounted for in the shape sensing model.